Subjectivity Analysis for Aspect-based Sentiment and Opinion Detection of YouTube Product Reviews

Abstract The advent of the internet has presented a novel paradigm for conducting consumer research and focus group investigations. Social media platforms enable organizations and entities to efficiently obtain and analyze public opinions regarding their products and services. This eliminates the need for extensive resource allocation towards traditional survey methods and information-gathering strategies. The present study investigates the potential of utilizing the YouTube platform as a valuable data source for product review analysis—our research centers on Apple Inc. and its most profitable product, the Apple iPhone. Using user feedback from review videos, we employ a lexicon-based subjectivity analysis methodology to identify statements articulating subjective viewpoints about the iPhone product. Traditional sentiment analysis methods categorize opinionated views into two broad categories: positive or negative. To assess the general market perception of the product, we employ SpaCy’s aspect extraction functionality to identify significant parts of the product that are subject to viewer critique. A BERTopic model processes the user’s text by using topic modeling techniques to extract latent themes that provide insights into the various detected characteristics. The BERTopic model has high efficiency and yields a coherence score of 69%.